Flat Exposure Properties of VIRCAM
Detectors

Document number VDF-TRE-IOA-00008-0013 (Draft 20061122)

Jim Lewis

1. Introduction

In this document we are looking at the flat properties
of some of the AIT data that was taken at RAL with the 16 VIRCAM
detectors. In what follows we are looking at the frames
VIRCAM_IMG_FLAT255_0043 to 56 and their associated dark frames,
VIRCAM_IMG_DARK255_0124 to 137. All were taken with single integrations
using torch in a darkened room. The exposure times for each frame
are:

Frame

Exposure time (s)

VIRCAM_IMG_FLAT255_0043

1

VIRCAM_IMG_FLAT255_0044*

10

VIRCAM_IMG_FLAT255_0045

5

VIRCAM_IMG_FLAT255_0046*

10

VIRCAM_IMG_FLAT255_0047

10

VIRCAM_IMG_FLAT255_0048*

10

VIRCAM_IMG_FLAT255_0049

15

VIRCAM_IMG_FLAT255_0050*

10

VIRCAM_IMG_FLAT255_0051

20

VIRCAM_IMG_FLAT255_0052*

10

VIRCAM_IMG_FLAT255_0053

25

VIRCAM_IMG_FLAT255_0054*

10

VIRCAM_IMG_FLAT255_0055

30

VIRCAM_IMG_FLAT255_0056*

10

The starred frames were taken in order to monitor the flux of the light
source over time. The dark frames have matching exposure times. Below
are the images
resulting from dark subtracting and combining all of the 10s exposures
in the list above.

Figure 1.1 Detector 1

Figure 1.2 Detector 2

Figure 1.3 Detector 3

Figure 1.4 Detector 4

Figure 1.5 Detector 5

Figure 1.6 Detector 6

Figure 1.7 Detector 7

Figure 1.8 Detector 8

Figure 1.9 Detector 9

Figure 1.10 Detector 10

Figure 1.11 Detector 11

Figure 1.12 Detector 12

Figure 1.13 Detector 13

Figure 1.14 Detector 14

Figure 1.15 Detector 15

Figure 1.16 Detector 16

2. Self Flat Correction

As a first test for the repeatability of the flats, we flat corrected
each of the 10s flats that went into the master 10s flat.
The figure below shows the result for one of the input frame (number
44) -- this is very typical.

Figure 2.1 Detector 1

Figure 2.2 Detector 2

Figure 2.3 Detector 3

Figure 2.4 Detector 4

Figure 2.5 Detector 5

Figure 2.6 Detector 6

Figure 2.7 Detector 7

Figure 2.8 Detector 8

Figure 2.9 Detector 9

Figure 2.10 Detector 10

Figure 2.11 Detector 11

Figure 2.12 Detector 12

Figure 2.13 Detector 13

Figure 2.14 Detector 14

Figure 2.15 Detector 15

Figure 2.16 Detector 16

The table below shows the normalised RMS of the flat corrected
data (all bad pixels removed from stats). This shows that the
small and large scale variation in the flat field is correctable to
within less that half a percent in most cases. There is some striping
visible on a few
of the images in figure 2 (detectors 2 and 3 for example) and this is
more easily seen by bumping up the contrast on a display server.
Removing the stripes using the VIRCAM destripe algorithm does improve
the RMS estimates slightly, but not to the point where it's worth
bothering in this case.

Detector

RMS

1

0.0048

2

0.0055

3

0.0059

4

0.0049

5

0.0042

6

0.0045

7

0.0043

8

0.0047

9

0.0038

10

0.0040

11

0.0042

12

0.0041

13

0.0044

14

0.0036

15

0.0037

16

0.0051

3. Linearity

The images mentioned above can be used to measure the linearity of each
of the detectors. The 10s exposures that were interspersed with the
others (starred in the first table) were used to measure the drift of
the light source during the series. The illumination of the dome for
the real VISTA telescope
has been specified to be constant to within a level of well below 1%,
so monitoring source drift will probably not be necessary during normal
operations. In the case of this AIT data, however the source did drift
by more than 3% during the course of the observations. The VIRCAM
recipe for linearity measurement works on images that have not be dark
corrected, so to simulate data taken with a steady light source each
exposure in the series was corrected by:

Removing the dark for that exposure

Multiplying the dark corrected image by a factor to account
for the source drift

Adding the dark back on

The correction factor for a given exposure was calculated by the ratio
of the flux of the first monitoring exposure divided by the flux of the
monitoring exposure taken just after the exposure of interest.

Although we did our best to correct the flickering of the light source,
it is obvious when looking closely at the data, that our simple linear
correction isn't quite enough and that we don't have enough information
to correct things any better. Looking at the curves in the figure below
also shows that the linearity curve for each detector is very
different. Modelling the curvature properly in many cases will require
more than the 7 observations we have, especially if the flux of the
light source is a little unreliable (again hopefully this won't be the
case at the telescope). The x and y axes respectively represent
exposure time and median flux in a small box on each detector. The line
is a linear fit to the first 3-4 points on the graph, as it shows the
curvature in each linearity relationship. Restricting the linearity fit
to the first 4 exposure times can at least get us a reasonable guess at
the level of the non-linearity in each chip at the nominal 10000 count
mark that we are using for a QC parameter.

figure 3.1 Detector 1

figure 3.2 Detector 2

figure 3.3 Detector 3

figure 3.4 Detector 4

figure 3.5 Detector 5

figure 3.6 Detector 6

figure 3.7 Detector 7

figure 3.8 Detector 8

figure 3.9 Detector 9

figure 3.10 Detector 10

figure 3.11 Detector 11

figure 3.12 Detector 12

figure 3.13 Detector 13

figure 3.14 Detector 14

figure 3.15 Detector 15

figure 3.16 Detector 16

For each detector a linearity fit is done for each channel. By and
large the results suggest that the linearity is independent of channel
number, although for some there does appear to be some significant
variation. Below is a table showing the average and standard deviation
non-linearity at 10000 counts over all channels in each detector. The
linearity recipe also sets a quality of fit parameter for each channel.
This is calculated by first using the fit coefficients and the input
flux to calculate a 'linear' corrected flux for each exposure. Then an
RMS is worked out which indicates the deviation of the exposure time vs
corrected flux from a linear relationship. This value is also included
in the table below.

Detector

Linearity at 10000 ADU (%)

Fit Quality (%)

1

1.30
+/- 0.09

0.52
+/- 0.04

2

2.09
+/- 0.11

0.53
+/- 0.03

3

2.60
+/- 0.42

0.51
+/- 0.03

4

2.14
+/- 0.18

0.55
+/- 0.06

5

1.87
+/- 0.11

0.52
+/- 0.03

6

1.69
+/- 0.07

0.58
+/- 0.02

7

1.32
+/- 0.05

0.53
+/- 0.01

8

2.23
+/- 0.24

0.61
+/- 0.03

9

2.12
+/- 0.11

0.64
+/- 0.01

10

1.75
+/- 0.06

0.57
+/- 0.02

11

3.26
+/- 0.52

0.71
+/- 0.03

12

1.61
+/- 0.05

0.57
+/- 0.02

13

5.98
+/- 0.34

0.62
+/- 0.08

14

2.04
+/- 0.03

0.60
+/- 0.01

15

1.60
+/- 0.02

0.49
+/- 0.01

16

2.45
+/- 0.11

0.65
+/- 0.01

In order to test whether the linearity correction is any good, we
did a linearity correction on all the 10s flats and combined them into
a master corrected 10s dome flat. The we linearity corrected a 5s
flat and divided it by our corrected master. The result is below in
Figure 3. The table below shows the RMS for the resulting
linearity corrected and flat fielded image. In order to compare this
result with the table above in section 2 we divided the RMS by sqrt(2)
to take into account the shot noise difference between this 5s exposure
and the 10s exposure presented above. This corrected RMS is comparable
to that from the self flat operation, indicating that by and large the
flat field correction is not degraded by the linearisation operation.
There are some small areas in, for example, detectors 4 and 13 which
raise some questions and these will have to be investigated at a later
time. A priority for daytime commissioning should be to redo this
exercise with the proper dome lines and with many more exposures in the
sequence.

figure 4.1 Detector 1

figure 4.2 Detector 2

figure 4.3 Detector 3

figure 4.4 Detector 4

figure 4.5 Detector 5

figure 4.6 Detector 6

figure 4.7 Detector 7

figure 4.8 Detector 8

figure 4.9 Detector 9

figure 4.10 Detector 10

figure 4.11 Detector 11

figure 4.12 Detector 12

figure 4.13 Detector 13

figure 4.14 Detector 14

figure 4.15 Detector 15

figure 4.16 Detector 16

Detector

RMS

RMS(10)

1

0.0075

0.0053

2

0.0087

0.0062

3

0.0091

0.0064

4

0.0107

0.0076

5

0.0070

0.0049

6

0.0068

0.0048

7

0.0069

0.0049

8

0.0079

0.0056

9

0.0064

0.0045

10

0.0070

0.0049

11

0.0073

0.0052

12

0.0067

0.0047

13

0.0090

0.0063

14

0.0060

0.0042

15

0.0062

0.0044

16

0.0085

0.0060

4. Bad Pixel Masks

The linearity_analyse recipe also writes out bad pixel masks. Figure 4
below shows a full grid of the masks for each detector. The actual
percentage of bad pixels is listed in the caption.